Telecommunication Fiber Box Detection Using YOLO in Urban Environment
Abstract
Keywords
Full Text:
PDFReferences
Wickramasinghe, S. R., & Razak, K. A. (2023). The Impact Of The Telecommunication Industry As AModerator on Poverty Alleviation and Educational Programmes To Achieve Sustainable Development Goals In Developing Countries. Journal of Informatics and Web Engineering, 2(1), 25-37. doi:https://doi.org/10.33093/jiwe.2023.2.1.3
Veligura, N., Chan, K. K.-K., Ingen, F. v., & Cufre, G. (2020, May). COVID-19’s Impact on the Global Telecommunications Industry. p. 6.
Department of Statistics Malaysia (DOSM). (2023). ICT Use and Access By Individuals And Households Survey Report 2022. Putrajaya: Department of Statistics Malaysia.
Neves, A. (2021, November 17). Basics to Help You Know Everything About Fiber Distribution Box. Retrieved from twoosk: https://blog.twoosk.com/telecommunications-equipment/basics-to-help-you-know-everything-about-fiber-distribution-box/
Sheldon. (2019, November 21). Fiber Distribution Panel Wiki, Types and Buying Tips. Retrieved from FS: https://community.fs.com/blog/fiber-distribution-panel-wiki-buying-tips.html
Du, L., Zhang, R., & Wang, X. (2020, May). Overview of two-stage object detection algorithms. In Journal of Physics: Conference Series (Vol. 1544, No. 1, p. 012033). IOP Publishing.
Zhiqiang, W., & Jun, L. (2017). A Review of Object Detection Based on Convolutional Neural Network. 2017 36th Chinese Control Conference (CCC) (pp. 11104-11109). IEEE.
Diwan, T., Anirudh, G., & Tembhurne, J. V. (2023). Object detection using YOLO: challenges, architectural successors, datasets and applications. Multimedia Tools and Applications, 82(6), 9243-9275. doi:https://doi.org/10.1007/s11042-022-13644-y
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2015). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE conference on computer vision and pattern recognition, (pp. 779-788).
Terven, J. R., & Cordova-Esparaza, D. M. (2023). A comprehensive review of YOLO: From YOLOv1 to YOLOv8 and beyond. 1-27. doi:https://doi.org/10.48550/arXiv.2304.00501
Olorunshola, O. E., Irhebhude, M. E., & Evwiekpaefe, A. E. (2023). A Comparative Study of YOLOv5 and YOLOv7 Object Detection Algorithms. Journal of Computing and Social Informatics, 2(1), 1-12. doi:http://dx.doi.org/10.33736/jcsi.5070.2023
Munawar, M. R. (2022, 12 4). FAQs on YOLOv5 and YOLOv7. Retrieved from medium: https://medium.com/augmented-startups/faqs-on-yolov5-and-yolov7
Guo, B., Zou, Y., Fang, Y., Goh, Y. M., & Zou, P. X. (2021). Computer vision technologies for safety science and management in construction: A critical review and future research directions. Safety Science, 135, 1-37. doi:http://dx.doi.org/10.1016/j.ssci.2020.105130
Paneru, S., & Jeelani, I. (2021). Computer vision applications in construction: Current state, opportunities & challenges. Automation in Construction, 132, 1-17. doi:https://doi.org/10.1016/j.autcon.2021.103940
Kolar, Z., Chen, H., & Luo, X. (2018). Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images. Automation in Construction, 89, 58-70. doi:https://doi.org/10.1016/j.autcon.2018.01.003
Fang, W., Zhong, B., Zhao, N., Love, P. E., Luo, H., Xue, J., & Xu, S. (2019). A deep learning-based approach for mitigating falls from height with computer vision: Convolutional neural network. Advanced Engineering Informatics, 39, 170-177. doi:https://doi.org/10.1016/j.aei.2018.12.005
Maharana, K., Mondal, S., & Nemade, B. (2022). A review: Data pre-processing and data augmentation techniques. Global Transitions Proceedings, 3(1), 91-99.
Anushkannan, N. K., Kumbhar, V. R., Maddila, S. K., & Kolli, C. S. (2022). YOLO Algorithm for Helmet Detection in Industries for Safety Purpose. 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), (pp. 225-230).
Dong, X., Yan, S., & Duan, C. (2022). A lightweight vehicles detection network model based on YOLOv5. Engineering Applications of Artificial Intelligence, 113, 1-14. doi:https://doi.org/10.1016/j.engappai.2022.104914
Wang, C.-Y., Liao, H.-Y. M., Yeh, I.-H., Wu, Y.-H., Chen, P.-Y., & Hsieh, J.-W. (2020). CSPNet: A New Backbone that can Enhance Learning Capability of CNN. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, (pp. 390-391). doi:https://doi.org/10.48550/arXiv.1911.11929
Xu, R., Lin, H., Lu, K., Cao, L., & Liu, Y. (2021). A Forest Fire Detection System Based on Ensemble Learning. Forests, 12(217), 1-17. doi:https://doi.org/10.3390/f12020217
Fang, C., Xiang, H., Leng, C., Chen, J., & Yu, Q. (2022). Research on Real-Time Detection of Safety Harness Wearing of Workshop Personnel Based on YOLOv5 and OpenPose. Sustainability, 14(10), 1-18. doi:https://doi.org/10.3390/su14105872
Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018). Path Aggregation Network for Instance Segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, (pp. 8759-8768). doi:https://doi.org/10.48550/arXiv.1803.01534
Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., & Savarese, S. (2019). Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, (pp. 658-666). doi:https://doi.org/10.48550/arXiv.1902.09630
DOI: http://dx.doi.org/10.18517/ijaseit.13.6.19027
Refbacks
- There are currently no refbacks.
Published by INSIGHT - Indonesian Society for Knowledge and Human Development